From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs
This research explores how multimodal large language models (LLMs) integrate auditory and visual information to make decisions. The study, titled "From Senses to Decisions," investigates the complex flow of perceptual data within these advanced AI systems.
A new study investigates the intricate processes by which multimodal large language models (LLMs) integrate sensory information. Titled "From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs," the research explores how these advanced AI systems interpret and utilize auditory and visual data to inform their decision-making. The paper was authored by Wish Suharitdamrong and three other collaborators. Its submission history dates to June 8, 2026. The paper is available in various formats, including PDF, HTML (experimental), and TeX Source. It also includes associated code, data, and media through platforms like alphaXiv, DagsHub, and Hugging Face. The study underscores the ongoing advancements in AI's ability to mimic and understand human-like perception, paving the way for more sophisticated and context-aware artificial intelligence. The research is part of a broader academic discourse, with tools for citation and related papers available via platforms like Semantic Scholar and CORE recommender.
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